Satellite observation of atmospheric CO2 and water storage change over Iran.

Like many other Middle East countries, Iran has been suffering from severe water shortages over the last two decades, as evidenced by significant decline in surface water and groundwater levels. The observed changes in water storage can be attributed to the mutually reinforcing effects of human activities, climatic variability, and of course the climate change. The objective of this study is to analyze the dependency of atmospheric CO2 increase on the water shortage of Iran, for which we investigate the spatial relationship between water storage change and CO2 concentration using large scale satellite data. We conduct our analysis using water storage change data from GRACE satellite and atmospheric CO2 concentration from GOSAT and SCIAMACHY satellites during 2002-2015. To analyze the long-term behavior of time series we benefit from Mann-Kendal test and for the investigation of the relationship between atmospheric CO2 concentration and total water storage we use Canonical Correlation Analysis (CCA) and Regression model. Our Results show that the water storage change anomaly and CO2 concentration are negatively correlated especially in northern, western, southwest (Khuzestan province), and also southeast (Kerman, Hormozgan, Sistan, and Baluchestan provinces) of Iran. CCA results reveal that in the most of northern regions, the decrease in water storage is significantly influenced by the increase of CO2 concentration. The results further show that precipitation in the highland and peaks does not seem to be influenced by the long and short-term variation in CO2 concentration. Besides, our results show that the CO2 concentration is slightly correlated with a weak positive trend in evapotranspiration over agricultural areas. Thus, the indirect effect of CO2 on increasing evapotranspiration is observed spatially in the whole of Iran. The results of the regression model between total water storage change and carbon dioxide (R2 = 0.91)/water discharge/water consumption show that carbon dioxide has the highest effect on total water storage change at large scale. The results of this study will contribute to both water resource management and mitigation plans to achieve the goal of CO2 emission reduction.

Time series analysis and spatial distribution map of aggregate risk index due to tropospheric NO2 and O3 based on satellite observation.

A high increase in human activities has led to more emission of air pollutants in metropolises and industrial areas. Recently, remotely sensed data of tropospheric pollutants is used for environmental management and decision-making on large scale. The purpose of this study was a time series analysis of nitrogen dioxide Vertical Column Density (NO2 VCD) and Ozone (O3) using Ozone Monitoring Instrument (OMI) from 2005 to 2016 by Mann-Kendall test. Also, the aggregate risk index (ARI) was calculated to estimate the overall impact of exposure to tropospheric NO2 and O3 concentrations at the national scale in 2016. To estimate the surface NO2 related drivers, The Radial Basis Function (RBF) neural network modeling was performed for different months of 2016. Results of Mann-Kendall test showed that tropospheric ozone concentration had an increasing trend in all parts of Iran and this increasing trend was significantly higher in the southern region of Iran and lower in the northern parts of Iran. NO2 VCD in most parts of Iran had a significant increasing trend. The result of sensitivity analysis showed that NO2 VCD (1.25), the distance to the industrial area, (1.20) and wind speed (1.07) were the most important variables for the estimation of surface NO2 concentration. Spatial ARI with the highest risks is mainly located in the Northern half of Iran, especially in Tehran, Alborz, and Khorasan-e- Razavi provinces, where NO2 and O3 concentrations are very severe. In northern Iran and central cities, the ARI values are calculated from 1.5 to 2.08, indicating the highest human health risks in these regions. The human health risks based on OMI observation were obtained higher in comparison to AQM data because the satellite data coverage is larger than AQM station and monitors transmitted air pollution by the wind in addition to local pollution. Based on this research, using satellite observation for air quality monitoring is a suitable tool for environmental management on a national scale.

Spatial distribution of XCO2 using OCO-2 data in growing seasons.

The purpose of this research is to assess the spatial distribution of CO2 concentration during the growing seasons (April to September) in 2015 over Iran. The XCO2 data belonging to orbiting carbon observatory-2 (OCO-2) and eight environmental variables data consist of normalized difference vegetation index (NDVI), net primary productivity (NPP), land surface temperature (LST), leaf area index (LAI), air temperature, wind speed, wind direction, and national land cover map were modeled by multi-layer perceptron (MLP). The values of R2 and RMSE indices show the good performance of the multi-layer perceptron model for monthly models. Based on sensitivity analysis results, land cover and wind direction had the most important role in the spatial distribution of XCO2. Also, the results revealed that the maximum values of XCO2 observed in the east, south east, and desert areas in central of Iran due to the lack of vegetation cover, lack of local wind current, and high temperature. The western, northwestern and northern regions of Iran have the minimum amounts of XCO2 because of existing valuable ecosystem such as Hyrcanian and Zagrous forests, rangeland, air currents, and low temperature. The findings of this study indicated that the manageable factors such as land cover and vegetation cover play very important roles in the spatial distribution of CO2 and finding carbon dioxide source and sink at national scale. Therefore, policymakers and managers by the logical management of these resources are able to control or even reduce the concentration of carbon dioxide in different areas.

Correction to: Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN.

The original version of this article unfortunately contained an error in the affiliation section.

Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN.

CO2 concentration (XCO2) shows the spatial and temporal variation in Iran. The major purpose of this investigation is the assessment of the spatial distribution of carbon dioxide concentration in the different seasons of 2013 based on the Thermal And Near Infrared Sensor for Carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) level 2 GOSAT data by implementing the ordinary kriging (OK) method. In this study, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) data from the MODerate resolution Imaging Spectroradiometer (MODIS), and metrological parameters (temperature and precipitation) were used for the analysis of the spatial distribution of CO2 over Iran in 2013. The spatial distribution maps of XCO2 show the highest concentration of this gas in the south and south-east and the lowest concentration in the north and north-west. These results indicate that the concentration of carbon dioxide decreased with the increase of LST and temperature and a decrease of NDVI and humidity in the study area. Therefore, the existence of vegetation has an effective role in capturing carbon from the atmosphere by photosynthesis phenomena, and sustainable land management can be effective for carbon absorption from the atmosphere and mitigation of climate change in arid and semi-arid regions.